Methods and systems for an artificial intelligence support network for vibrant constitutional guidance
A system for an artificial intelligence support network for informed advisor guidance includes a diagnostic engine operating on a computing device and configured to receive a biological extraction related to a user, said biological extraction comprising a self-assessment of the user, and generate a diagnostic output as a function of the self-assessment of the user The system includes an advisor module. The advisor module is configured to select an informed advisor as a function of the diagnostic output, generate an advisory output as a function of the diagnostic output, said advisory output identifying the current condition of the user, and transmit the advisory output to a client device associated with the selected informed advisor.
1. A system for an artificial intelligence support network for behavior modification, the system comprising at least a server, the at least a server designed and configured to
receive at least a biological extraction from a user;
train, iteratively, a machine learning model using a first training data set, wherein the first training data set comprises at least a prognostic label correlated to at least an element of physiological state datum;
train, iteratively, a machine learning process using a second training data set, wherein the second training data set comprises longitudinal data for the user correlated to ameliorative process labels;
generate a diagnostic output based on the at least a biological extraction using the machine learning model, wherein generating the diagnostic output comprises:
generating at least an ameliorative process label;
ranking the at least an ameliorative process label as a function of the machine learning process;
determining at least a correct ameliorative process as a function of the ranking; and
generating the diagnostic output, wherein the diagnostic output comprises the at least a correct ameliorative process;
generate at least a request for a behavior modification as a function of the at least a correct ameliorative process label; and
identify an influencer as a function of the at least a request for a behavior modification, wherein identifying the at least an influencer further comprises:
determining a plurality of influencers based on the request for behavior modification;
receiving, for each influencer of the plurality of influencers, a set of influencer attributes; and
matching a set of user attributes to the set of influencer attributes corresponding to the influencer.
2. The system of claim 1 , wherein the at least a server is designed and configured to receive a first training data set including a plurality of first data entries, each first data entry of the plurality of first data entries including at least an element of physiological state data and at least a correlated first prognostic label.
3. The system of claim 1 , wherein the at least a server is designed and configured to receive a second training data set including a plurality of second data entries, each second data entry of the plurality of second data entries including at least a second prognostic label and at least a correlated ameliorative process label.
4. The system of claim 1 , wherein the at least a server generates the at least a request for a behavior modification as a function of a user requested category of behavior change.
5. The system of claim 1 , wherein the at least a server is configured to receive a user requested category of behavior change from a user client device.
6. The system of claim 1 , wherein the at least a server is configured to receive a user requested category of behavior change from an advisory client device.
7. The system of claim 1 , wherein the at least a server is configured to determine the plurality of influencers as a function of a user-requested category of at least an influencer.
8. The system of claim 1 , wherein the at least a server is further configured to match the set of user attributes to the set of influencer attributes by performing a classification algorithm matching the set of user attributes to the set of influencer attributes.
9. The system of claim 8 , wherein the classification algorithm classifies a set of problematic behavior attributes of the user to a set of positive qualities of the influencer.
10. The system of claim 8 , wherein the classification algorithm classifies a set of beliefs of a user to a set of beliefs of an influencer.
11. A method of implementing an artificial intelligence support network for behavior modification, the method comprising
receiving, by at least a server, at least a biological extraction from a user;
training, iteratively, a machine learning model using a first training data set, wherein the first training data set comprises at least a prognostic label correlated to at least an element of physiological state datum;
training, iteratively, a machine learning process using a second training data set, wherein the second training data set comprises longitudinal data for the user correlated to ameliorative process labels;
generating, by the at least a server, a diagnostic output based on the at least a biological extraction using the machine learning model, wherein generating the diagnostic output comprises:
generating at least an ameliorative process label;
ranking the at least an ameliorative process label as a function of the machine learning model;
determining at least a correct ameliorative process as a function of the ranking; and
generating the diagnostic output, wherein the diagnostic output comprises the at least a correct ameliorative process;
generating, by the at least a server, at least a request for a behavior modification as a function of the at least a correct ameliorative process label; and
identifying, by the at least a server, an influencer as a function of the at least a request for a behavior modification, wherein identifying the at least an influencer further comprises:
determining a plurality of influencers based on the request for behavior modification;
receiving, for each influencer of the plurality of influencers, a set of influencer attributes; and
matching a set of user attributes to the set of influencer attributes corresponding to the influencer.
12. The method of claim 11 , wherein generating the diagnostic output further comprises receiving a first training data set including a plurality of first data entries, each first data entry of the plurality of first data entries including at least an element of physiological state data and at least a correlated first prognostic label.
13. The method of claim 11 , wherein generating the diagnostic output further comprises receiving a second training data set including a plurality of second data entries, each second data entry of the plurality of second data entries including at least a second prognostic label and at least a correlated ameliorative process label.
14. The method of claim 11 , wherein generating the at least a request for behavior modification further comprises generating the at least a request for a behavior modification as a function of a user requested category of behavior change.
15. The method of claim 11 , wherein generating the at least a request for behavior modification further comprises receiving a user requested category of behavior change from a user client device.
16. The method of claim 11 , wherein generating the at least a request for behavior modification further comprises receiving a user requested category of behavior change from an advisory client device.
17. The method of claim 11 , wherein determining the plurality of influencers further comprises determining the plurality of influencers as a function of a user-requested category of at least an influencer.
18. The method of claim 11 , wherein matching the set of user attributes to the set of influencer attributes by performing a classification algorithm matching the set of user attributes to the set of influencer attributes.
19. The method of claim 18 , wherein the classification algorithm classifies a set of problematic behavior attributes of the user to a set of positive qualities of the influencer.
20. The method of claim 18 , wherein the classification algorithm classifies a set of beliefs of a user to a set of beliefs of an influencer.